In [12]:
import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os

# Plotly to create interactive graph
import chart_studio.plotly as py
from plotly import tools
from plotly.offline import init_notebook_mode,iplot
init_notebook_mode(connected=False)
import plotly.figure_factory as ff
import plotly.graph_objs as go

%matplotlib inline
sns.set_style("whitegrid")
plt.style.use("fivethirtyeight")

# To remove un-necessary warnings
import warnings
warnings.filterwarnings("ignore")

deliveries = pd.read_csv('IPL Ball-by-Ball 2008-2020.csv')
matches = pd.read_csv('IPL Matches 2008-2020.csv')
In [13]:
x=['Sunrisers Hyderabad', 'Mumbai Indians', 'Gujarat Lions',
    'Rising Pune Supergiant', 'Royal Challengers Bangalore',
    'Kolkata Knight Riders', 'Delhi Daredevils', 'Kings XI Punjab',
    'Chennai Super Kings', 'Rajasthan Royals', 'Deccan Chargers',
    'Kochi Tuskers Kerala', 'Pune Warriors', 'Rising Pune Supergiants', 'Delhi Capitals']

y = ['SRH','MI','GL','RPS','RCB','KKR','DC','KXIP','CSK','RR','SRH','KTK','PW','RPS','DC']

matches.replace(x,y,inplace = True)
deliveries.replace(x,y,inplace = True)
In [14]:
matches['season'] = matches['date'].str[:4].astype(int)
data = [go.Histogram(x=matches['season'], marker=dict(color='#EB89B5', line=dict(color='#000000', width=1)), opacity=0.75)]
layout = go.Layout(title='Matches In Every Season ',xaxis=dict(title='Season',tickmode='linear'),
                    yaxis=dict(title='Count'),bargap=0.2, plot_bgcolor='rgb(245,245,245)')

fig = go.Figure(data=data, layout=layout)
iplot(fig)
In [15]:
matches_played=pd.concat([matches['team1'],matches['team2']])
matches_played=matches_played.value_counts().reset_index()
matches_played.columns=['Team','Total Matches']
matches_played['wins']=matches['winner'].value_counts().reset_index()['winner']

matches_played.set_index('Team',inplace=True)
totm = matches_played.reset_index().head(8)

trace = go.Table(
    header=dict(values=["Team","Total Matches","Wins"],
                fill = dict(color='#ff96ea'),
                font = dict(color=['rgb(45, 45, 45)'] * 5, size=14),
                align = ['center'],
               height = 30),
    cells=dict(values=[totm['Team'], totm['Total Matches'], totm['wins']],
               fill = dict(color=['rgb(235, 193, 238)', 'rgba(228, 222, 249, 0.65)']),
               align = ['center'], font_size=13, height=25))

layout = dict(
    width=750,
    height=420,
    autosize=False,
    title='Total Matches vs Wins per team',
    margin = dict(t=100),
    showlegend=False,    
)
fig1 = dict(data=[trace], layout=layout)
iplot(fig1)
In [16]:
trace1 = go.Bar(x=matches_played.index,y=matches_played['Total Matches'],
                name='Total Matches',opacity=0.4)

trace2 = go.Bar(x=matches_played.index,y=matches_played['wins'],
                name='Matches Won',marker=dict(color='red'),opacity=0.4)

trace3 = go.Bar(x=matches_played.index,
               y=(round(matches_played['wins']/matches_played['Total Matches'],3)*100),
               name='Win Percentage',opacity=0.6,marker=dict(color='gold'))

data = [trace1, trace2, trace3]

layout = go.Layout(title='Match Played, Wins And Win Percentage',xaxis=dict(title='Team'),
                   yaxis=dict(title='Count'),bargap=0.2,bargroupgap=0.1, plot_bgcolor='rgb(245,245,245)')

fig = go.Figure(data=data, layout=layout)
iplot(fig)
In [17]:
win_percentage = round(matches_played['wins']/matches_played['Total Matches'],3)*100
win_percentage.head(3)
Out[17]:
Team
MI     59.1
SRH    53.3
RCB    50.8
dtype: float64
In [18]:
venue_matches=matches.groupby('venue').count()[['id']].sort_values(by='id',ascending=False).head()
ser = pd.Series(venue_matches['id']) 
venue_matches=matches.groupby('venue').count()[['id']].reset_index()

data = [{"y": venue_matches['id'],"x": venue_matches['venue'], 
          "marker": {"color": "lightblue", "size": 12},
         "line": {"color": "red","width" : 2,"dash" : 'dash'},
          "mode": "markers+lines", "name": "Women", "type": "scatter"}]

layout = {"title": "Stadiums Vs. Matches", 
          "xaxis": {"title": "Matches Played", }, 
          "yaxis": {"title": "Stadiums"},
          "autosize":False,"width":900,"height":700,"plot_bgcolor":"rgb(245,245,245)"}

fig = go.Figure(data=data, layout=layout)
iplot(fig)
In [19]:
data = [go.Bar(
    x = matches["toss_decision"].value_counts().index,
    y = matches["toss_decision"].value_counts().values,
    marker = dict(line=dict(color='#000000', width=1))
)]

layout = go.Layout(
   {
      "title":"Most Likely Decision After Winning Toss",
       "xaxis":dict(title='Decision'),
       "yaxis":dict(title='Number of Matches'),
       "plot_bgcolor":'rgb(245,245,245)'
   }
)
fig = go.Figure(data=data,layout = layout)
iplot(fig)
In [20]:
batsmen = matches[['id','season']].merge(deliveries, left_on = 'id', right_on = 'id', how = 'left').drop('id', axis = 1)
season=batsmen.groupby(['season'])['total_runs'].sum().reset_index()

avgruns_each_season=matches.groupby(['season']).count().id.reset_index()
avgruns_each_season.rename(columns={'id':'matches'},inplace=1)
avgruns_each_season['total_runs']=season['total_runs']
avgruns_each_season['average_runs_per_match']=avgruns_each_season['total_runs']/avgruns_each_season['matches']
In [21]:
Season_boundaries=batsmen.groupby("season")["batsman_runs"].agg(lambda x: (x==6).sum()).reset_index()
fours=batsmen.groupby("season")["batsman_runs"].agg(lambda x: (x==4).sum()).reset_index()
Season_boundaries=Season_boundaries.merge(fours,left_on='season',right_on='season',how='left')
Season_boundaries=Season_boundaries.rename(columns={'batsman_runs_x':'6"s','batsman_runs_y':'4"s'})

Season_boundaries['6"s'] = Season_boundaries['6"s']*6
Season_boundaries['4"s'] = Season_boundaries['4"s']*4
Season_boundaries['total_runs'] = season['total_runs']

trace1 = go.Bar(
    x=Season_boundaries['season'],
    y=Season_boundaries['total_runs']-(Season_boundaries['6"s']+Season_boundaries['4"s']),
    marker = dict(line=dict(color='#000000', width=1)),
    name='Remaining runs',opacity=0.6)

trace2 = go.Bar(
    x=Season_boundaries['season'],
    y=Season_boundaries['4"s'],
    marker = dict(line=dict(color='#000000', width=1)),
    name='Run by 4"s',opacity=0.7)
trace3 = go.Bar(
    x=Season_boundaries['season'],
    y=Season_boundaries['6"s'],
    marker = dict(line=dict(color='#000000', width=1)),
    name='Run by 6"s',opacity=0.7)


data = [trace1, trace2, trace3]
layout = go.Layout(title="Run Distribution per year",barmode='stack',xaxis = dict(tickmode='linear',title="Year"),
                                    yaxis = dict(title= "Run Distribution"), plot_bgcolor='rgb(245,245,245)')

fig = go.Figure(data=data, layout=layout)
iplot(fig)
In [22]:
high_scores=deliveries.groupby(['id', 'inning','batting_team','bowling_team'])['total_runs'].sum().reset_index() 
high_scores=high_scores[high_scores['total_runs']>=200]
hss = high_scores.nlargest(10,'total_runs')

trace = go.Table(
    header=dict(values=["Inning","Batting Team","Bowling Team", "Total Runs"],
                fill = dict(color = 'red'),
                font = dict(color = 'white', size = 14),
                align = ['center'],
               height = 30),
    cells=dict(values=[hss['inning'], hss['batting_team'], hss['bowling_team'], hss['total_runs']],
               fill = dict(color = ['lightsalmon', 'rgb(245, 245, 249)']),
               align = ['center'], font_size=13))

layout = dict(
    width=830,
    height=410,
    autosize=False,
    title='Highest scores of IPL',
    showlegend=False,    
)

fig1 = dict(data=[trace], layout=layout)
iplot(fig1)
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